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This paper has predicted digital supply chain (DSC) adoption through the role of firms' proactiveness, knowledge management capability (KMC), innovation ambidexterity and the moderating effect of environmental dynamism.

Survey data from 354 managers in manufacturing and service firms were analysed using the PLS-SEM model.

The present study's findings ascertained the significant role of innovation ambidexterity in influencing DSC adoption, given that innovation exploration's direct and mediating impacts were greater than innovation exploitation's. Firms' proactiveness had the highest path coefficient value among the endogenous variables as an indispensable source for firms to successfully embrace KMC and innovation ambidexterity. The role of firm size on DSC adoption was also significant, revealing that SMEs were more likely than larger firms to adopt DSC practices. Despite its significant effect on innovation ambidexterity, KMC surprisingly exhibited no direct influence on DSC adoption. Furthermore, the findings demonstrated the significant moderating role of environmental dynamism on the effect of KMC on innovation exploration.

This research endeavour has presented valuable insights for scholars and managers, furnishing them with a framework to facilitate decision-making processes regarding adopting DSC practices. A key insight gleaned from this study has been the remarkable value of firms' proactive behaviour and innovation ambidexterity in facilitating DSC adoption decisions. Such adoption has empowered organisations to deploy appropriate interventions and allocate resources efficiently, considering the pivotal role of innovation exploration in contrast to the relatively limited impact of innovation exploitation within this context. Managers may also underscore the significance of firm size in shaping DSC adoption decisions. Small- and medium-sized enterprises (SMEs) have been more likely to embrace DSC initiatives than their larger counterparts. The agile scale and streamlined organisational structures of SMEs often translate into faster decision-making processes, allowing for DSC adoption with relative ease. SMEs might also exhibit a greater openness to reap the benefits associated with DSC systems, such as improved operational efficiency and cost reduction.

The present study has advanced DSC adoption research by examining innovation ambidexterity, knowledge management capability and firms' proactiveness. It has also provided valuable insights for scholars and managers, presenting a framework for decision-making processes regarding DSC adoption in an emerging economy context.

A digital supply chain (DSC) positively enhances circular economy (CE) practices. However, what factors and conditions lead to the implementation of DSC for transitioning toward CE is not yet clear. Therefore, this study aims at identifying and subsequently analyzing the antecedents of DSC for CE.

The study identifies major antecedents of DSC for CE to achieve sustainability objectives through literature review and expert opinions. In this study, 19 potential antecedents of DSCs for CE are established from the literature and suggestions from industry professionals. A trapezoidal fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is applied quantitatively to investigate the antecedents identified.

Conducted in the context of Indian automobile manufacturing industry, the findings of the study reflect that advanced information sharing arrangement, effective government policies for DSC and CE implementation and digitalizing the supply chains are the top three potential antecedents of DSC for a CE.

In the existing literature, few studies are specific to investigating the DSC and CE paradigm. The present study will help organizations develop a practical and integrated strategic approach that will foster DSC through improved knowledge of CE.

This study investigates the enablers and challenges of digital supply chains (DSCs) adoption and develops a digital supply chain maturity (DSCM) model as a basis for developing guidelines for DSC adoption in the digital transformation journey.

The research involves a systematic literature review (SLR) of Industry 4.0 (I4) adoption in supply chain (SC) practices to identify key enablers and associated maturity levels. The literature search of published articles during the 1997–2020 period and subsequent screening resulted in 64 articles. A DSCM model was developed using the categorization of important enablers and associated levels transitioning from the traditional SC to the DSC ecosystem.

Four broader categories of DSC enablers and challenges were identified from the content analysis of SLR. Digital strategy alongside I4 technologies and human capital were prominent in DSC adoption as I4 technologies and human capital depend on other enablers such as dynamic capabilities (DCs). Lack of infrastructure and financial constraints to implementing I4 were significant challenges in the DSC adoption.

The proposed DSCM model provides a holistic view of enablers and maturity levels from traditional SC to DSC adoption. However, the DSCM model needs to be empirically validated and streamlined further using inputs from practitioners.

The proposed DSCM model can be used as a framework to guide practitioners in assessing maturity and developing implementation plans for successful DSC adoption.

This research introduces a novel DSC maturity model through a holistic view of enablers and maturity levels from traditional SC to DSC adoption.

In this digital age, the rapid technological innovation and adoption, with the increasing use of big data analytics, has raised concerns about the ability of small and medium enterprises (SMEs) to sustain the competition and innovation performance (IP). To narrow the research gap, this paper investigates the role of big data analytics capability (BDAC) in moderating the relationship between digital innovation (DI) and SME innovation performance.

This research has been carried forward through a detailed theory and literature analysis. Data were analyzed through confirmatory factor analysis and structural equation models using a two-stage approach in smartPLS-4.

Results highlight that digital service capability (DSC) significantly mediates the relationship between DI and IP. Additionally, value co-creation (VCC) directly affects digital transformation (DT), while DI has a stronger effect on DSC than IP. Furthermore, BDAC significantly moderates the relation between DSC → IP and DT → IP, whereas it has a detrimental effect on the relation between DI and IP. In addition to that, VCC, DSC, DT, DI and BDAC have a direct, significant and positive effect on IP.

This research was motivated by the practical relevance of supporting SMEs in adopting DT and the resource-based view (RBV) and technology acceptance model (TAM). This study shows that all direct and indirect measures significantly affect innovation performance, including BDAC as moderator. These findings refresh the perspective on what DT, DI, VCC, DSC and BDAC can bring to a firm's innovation performance.

This paper has contributed to DT by empirically validating a theoretical argument that suggests the acceptance and adoption of new technology. This paper aims to fill theoretical gaps in understanding BDAC and DT by incorporating the RBV and TAM theories on BDAC and DT.

There has been limited research that has explored the connection between digital supply chain (DSC) and SC innovation and SC dynamic capabilities. This paper aims to examine the mediating effect of SC innovation on the relationship between DSC and SC dynamic capabilities.

The research model and hypotheses were tested, employing (Statistical Package of Social Sciences) SPSS 25.0 and (Analysis of Moment Structures) AMOS 24.0 on data drawn from the Chinese manufacturing enterprises.

The study reveals that DSC has a significant positive effect on SC innovation and SC dynamic capabilities. SC innovation also has a significant positive effect on SC dynamic capabilities. Besides, the authors' research illustrates that SC innovation partially mediates the relationship between DSC and SC dynamic capabilities.

Since the results are derived from the data collected from China, it may not, therefore, be generalized to other settings. Moreover, future research could consider other contextual variables such as “environmental uncertainty” and “Government's Reward-Penalty Mechanism,” which may influence SC dynamic capabilities.

The study provides practical insights for senior executives and managers in the manufacturing industry. Managers should emphasize the investment of advanced digital technologies and tools (DTTs) and improvement of SC visibility and collaboration. In the digital age, companies should pay attention to the introduction of advanced technologies, tools and processes and focus on cultivating an innovative spirit to promote SC dynamic capabilities, thereby enhancing competitive advantages.

The paper illustrates that DSC is of great significance to improving SC dynamic capabilities. This study reveals compelling insights for firms to enhance SC innovation and dynamic capabilities by using DSC as an enabler.

In the age of Industry 4.0 (I4.0), digital technologies (DTs) and the technologies' application in supply chain activities have become more important. On the other hand, global pressures for corporate social responsibility in the sustainable production of products are increasing. Accordingly, the purpose of this research is to develop and validate an instrument to measure the impact of digital supply chain (DSC) activities on the sustainable performance of manufacturing companies.

In the first step, through an in-depth study of the relevant literature, a conceptual model was developed and a questionnaire containing 51 indicators was designed. The questionnaire was distributed among 356 top managers and experts of the Iranian manufacturing companies, whereby finally 233 sound questionnaires were returned, yielding a response rate of about 64%. Exploratory factor analysis (EFA) was used to identify constructs and sub-constructs and the relationship between them was investigated using the partial least squares structural equation model (PLS-SEM) method.

Based on the obtained results, three constructs were identified, namely main activities (including sub-constructs: digital supplier, digital manufacturing, digital logistics and innovation and digital customer), support activities (with sub-constructs digital performance, DT and digital human resources) and sustainable performance (with sub-constructs of economic sustainability, environmental sustainability and social sustainability). The designed tool has excellent psychometric properties. The values of t-statistic = 11.07 and β = 0.602 indicate the positive impact of the DSC main activities on sustainable performance. Similarly, t = 2.42 and β = 0.149 prove that DSC support activities have a positive impact on sustainable performance. Also, based on the obtained values (t = 13.16 and β = 0.629), support activities have a significant impact on the main activities of the DSC. Finally, based on the calculated goodness-of-fit (GoF) index value (0.522), this paper concluded that the proposed model has high credibility.

Validation of the model is based on the answers received from the Iranian manufacturing companies. Therefore, caution should be taken regarding the generalizability of the results.

The proposed model presents a holistic view of the application of DTs in the supply chain and the DTs' impact on sustainable performance which might help manufacturing companies, particularly the Iranian companies to obtain a broader knowledge of the importance of DTs and DTs' usage toward responding to the challenges of today's complex business environment.

This study is among the first of the study's kind that attempts to develop and validate an instrument to measure the impact of DSC activities on the sustainable performance of manufacturing companies.

Using an abductive perspective, this study aims to review the scientific literature about the governance and management of the digital supply chain (DSC) in the context of the business organizations, providing an overview of the state of the art of the research and outlining a future research agenda with a knowledge management (KM) focus.

After investigating the Scopus database, 54 articles were identified as relevant and then subjected to an initial discernment. After this assessment, 34 articles focusing on operations management were further analyzed through both a bibliometric analysis and a content analysis.

The DSC represents a research area of increasing attention, with relevant contributions to several aspects of the field, as well as about KM. At the same time, the results show that the scientific literature on DSC models, solutions and applications is fragmented. Although the analysis has found a heterogeneous literature, two main streams of research seem to emerge: KM in the business culture development about DSC and KM in the business technological evolution about DSC.

Although there exists growing interest in the scientific community, or perhaps because of this, area of research remains fragmented and under-theorized, thus requiring more systematic studies considering both economic and social aspects of the DSC. This study aims to provide innovative insights about this evolution, especially highlighting the two main contributions of KM in DSCs that have been revealed (business culture development and business technological evolution).

The study examines the key drivers of the adoption of disruptive technologies (DTs) in the digital supply chain (DSC) in developing nations.

The data were collected using well-established measures grounded in the diffusion of innovation (DOI) theory. The hypotheses were tested using the structural equation modeling (SEM) approach using SmartPLS. The authors control for the demographics and apply the required statistical diagnostics for robust findings.

The compatibility and IT expertise were the two key factors in adopting the DTs in DSC in developing nations. The organizations with higher compatibility and internal IT expertise and competence witness a higher level of adoption of DT. The perceived cost and complexity were not found statistically significant. This may be probably because developing nations such as India do not perceive the technology adoption complex.

The research enhances DTs adoption, assuming it is organizational innovation. This study makes a theoretical contribution to the DOI literature.

The practicing managers should pay attention to addressing the existing technology compatibility issues and spend efforts on training employees to increase the IT expertise to improve the adoption of DT.

The greater adoption of the DTs in DSCs can reduce wastages in supply chains by a faster sense and response and greater technological flexibility with transparency and information sharing.

The key antecedent to the acceptance of the DTs in developing nations is compatibility than complexity and IT expertise than the cost. The study's originality lies in the fact that most studies on technology adoption study a single technology, but this study captures a holistic view on a group of technologies under industry 4.0.

Sai Coating, a small entrepreneurial firm, was one of the three firms that had received the license from ARCI for marketing the Detonation Spray Coating (DSC). Sai Coating made and sold the detonation gun (D-Gun) to three sectors, namely: Wire Drawing, Textiles and Aero components. The coating enhanced the life of the coated wire or surface and its functionality in some ways. The firm had a turnover of INR 4,500,000 and was looking to generate scale and maximize its revenues. The case revolves around the pricing strategy to be adopted by Sai coating to extract value from different set of customers. What should be the price levels given the nature of the product?

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.